摘要
粒子群优化(PSO)算法是一类新兴的随机优化技术,其思想来源于人工生命和演化计算理论。PSO通过粒子追随个体极值和全局极值来完成优化。本文借鉴生物界中雁群的飞行特征,给出了一种改进的PSO算法。该算法一方面将粒子排序,每个粒子跟随其前面那个较优粒子飞行,保持了多样性;另一方面使每个粒子利用更多其他粒子的有用信息,加强粒子之间的合作与竞争。用3个基准函数对新算法进行实验,结果表明,新算法不仅具有更好的收敛精度和更快的收敛速度,而且能更有效地进行全局搜索。
particle swarm optimization （PSO） is a new stochastic optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best solution of the whole swarm. In this paper, an improved algorithm is proposed using the characteristics of the flight of geese for reference. The improved algorithm has superiority over PSO; for one thing, it keeps the population various by ordering all the particles and making each particle fly following its anterior particle; for another thing, it strengthens cooperation and competition between particles by making each particle share more useful information of the other particles. Three benchmark functions are tested and the experimental results show that the new algorithm not only significantly speed up the convergence, but also effectively solve the premature convergence problem.
出处
《计算机科学》
CSCD
北大核心
2006年第11期166-168,191,共4页
Computer Science
关键词
群体智能
粒子群优化
惯性权重线性下降
雁群飞行
Swarm intelligence,Particle swarm optimization,Linearly decreasing inertia weight,Flight of geese
